The subject matter disclosed herein generally relates to analyzing the expression of biomarkers in cells that are examined in situ in their tissue of origin. More particularly, the disclosed subject matter relates to an automated determination of cell-by-cell segmentation quality of a tissue sample.
The expression of biomarkers in cells and tissues has been an important technique for biological and medical research relating to, for example, drug development, disease pathways, tissue pathology and clinical studies. Available biomarkers allow for the determination of (1) a specific cell or components of a cell such as cytoplasm, membrane or nucleus or (2) the morphology of a cell including, for example, identifying the shape, structure, form, and size of a cell, both based on the level of expression of a given biomarker. Historically, tissue treated with several biomarkers that each emanates different signals has been analyzed using digital imagery. However, more recently, techniques have been developed that allow for the examination of a single specimen using a greater number of biomarkers, thus providing more information and data available for analysis. Sequential multiplexing techniques involve staining a specimen using a fluorophore labeled probe to indicate the expression of one or more probe bound biomarkers, chemically bleaching the specimen and re-staining the specimen with a different probe bound biomarker. A probe bound biomarker may also be referred to as a “biomarker.”
Sequential multiplexing technology used, for example, in the GE Healthcare MultiOmyx™ platform has enabled researchers to perform studies in which a large number of biomarkers (60+) can be analyzed at the cell level. Such technology allows a single tissue sample to be examined and data collected sequentially using different biomarkers.
The analysis of a typical multiplexing study may take several weeks to months depending on the sample size and number of biomarkers used. As part of the process, cell segmentation accuracy can significantly affect the quality of the subsequent biomarker quantification and data analysis. For example, FIGS. 1A and 1B show subcellular distributions (blue: nuclear, green: cytoplasm, red: membrane) of a cytoplasmic marker (HSP90) image using the GE Healthcare MultiOmyx™ platform. FIG. 1A illustrates the distribution when including all of the cells in the image and FIG. 1B illustrates the distribution after applying cell filtering based on the cell size and the presence of a nucleus (i.e. cells with no nuclei were removed). The cell filtering in FIG. 1B improves the localization of the biomarker by increasing the disparity between cytoplasm, membrane and nuclear distributions. Cell raw and segmentation images using structural biomarkers, for example, are shown in FIGS. 2A-2D using the GE Healthcare MultiOmyx™ platform. FIG. 2A is a low resolution image of an input composite raw image showing three channels of biomarkers for nucleus, membrane and cytoplasm and FIG. 2B is a higher resolution of the composite raw image of FIG. 2A. The composite raw image is the assembly of the three visual images of the three separately biomarker stained tissue sample and may also be referred to as a multi-channel or multi-color raw image. FIG. 2C is a low resolution image of an output composite segmentation image showing cell boundaries in white as well as blue, green and red that represent the cell nuclear, cytoplasm and membrane compartments, respectively as determined by such biomarkers and FIG. 2D is a higher resolution of the composite segmentation image of FIG. 2C. A composite segmentation image is the result of an analyzer, such as a MultiOmyx™ system, analyzing the composite raw image and visually identifying the cell segments of the tissue sample in the composite raw image. A composite segmentation image may also be referred to as a multi-channel or multi-color segmentation image. When zooming in to the cell level (as shown in the higher resolution images of FIG. 2B and FIG. 2D), it becomes clear that the segmentation is “poor” for some of the cells by showing image errors present for those cells with “poor” segmentation that would be evident to an expert such as, for example, a pathologist or cell biologist. Performing a manual cell-by-cell segmentation review is one method of reducing such errors, but it is time consuming and tiring for the person doing the review.